{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T21:21:32Z","timestamp":1789161692281,"version":"build-2803163510"},"reference-count":71,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T00:00:00Z","timestamp":1769644800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Smart devices and multimodal biosignal systems, including electroencephalography (EEG\/MEG), ECG-derived heart rate variability (HRV), and electromyography (EMG), increasingly supported by artificial intelligence (AI), are being explored to improve the assessment and longitudinal monitoring of mental health conditions. Despite rapid growth, the available evidence remains heterogeneous, and clinical translation is limited by variability in acquisition protocols, analytical pipelines, and validation quality. This systematic review synthesizes current applications, signal-processing approaches, and methodological limitations of biosignal-based smart systems for mental health monitoring. Methods: A PRISMA 2020-guided systematic review was conducted across PubMed\/MEDLINE, Scopus, the Web of Science Core Collection, IEEE Xplore, and the ACM Digital Library for studies published between 2013 and 2026. Eligible records reported human applications of wearable\/smart devices or multimodal biosignals (e.g., EEG\/MEG, ECG\/HRV, EMG, EDA\/GSR, and sleep\/activity) for the detection, monitoring, or management of mental health outcomes. The reviewed literature after predefined inclusion\/exclusion criteria clustered into six themes: depression detection and monitoring (37%), stress\/anxiety management (18%), post-traumatic stress disorder (PTSD)\/trauma (5%), technological innovations for monitoring (25%), brain-state-dependent stimulation\/interventions (3%), and socioeconomic context (7%). Across modalities, common analytical pipelines included artifact suppression, feature extraction (time\/frequency\/nonlinear indices such as entropy and complexity), and machine learning\/deep learning models (e.g., SVM, random forests, CNNs, and transformers) for classification or prediction. However, 67% of studies involved sample sizes below 100 participants, limited ecological validity, and lacked external validation; heterogeneity in protocols and outcomes constrained comparability. Conclusions: Overall, multimodal systems demonstrate strong potential to augment conventional mental health assessment, particularly via wearable cardiac metrics and passive sensing approaches, but current evidence is dominated by proof-of-concept studies. Future work should prioritize standardized reporting, rigorous validation in diverse real-world cohorts, transparent model evaluations, and ethics-by-design principles (privacy, fairness, and clinical governance) to support translation into practice.<\/jats:p>","DOI":"10.3390\/bioengineering13020165","type":"journal-article","created":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T09:12:01Z","timestamp":1769677921000},"page":"165","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Smart Devices and Multimodal Systems for Mental Health Monitoring: From Theory to Application"],"prefix":"10.3390","volume":"13","author":[{"given":"Andreea Violeta","family":"Caraga\u021b\u0103","sequence":"first","affiliation":[{"name":"Faculty of Mechanical, Industrial and Maritime Engineering, Ovidius University of Constanta, 900573 Constanta, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0410-0530","authenticated-orcid":false,"given":"Mihaela","family":"Hnatiuc","sequence":"additional","affiliation":[{"name":"Department Electronic and Telecommunication, Constanta Maritime University, 900663 Constanta, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oana","family":"Geman","sequence":"additional","affiliation":[{"name":"Division of Data Science and Artificial Intelligence, Computer Science and Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7028-3921","authenticated-orcid":false,"given":"Simona","family":"Halunga","sequence":"additional","affiliation":[{"name":"Faculty of Electronics, Telecommunications and Information, Technology, National University for Science and Technology, POLITEHNICA Bucharest, 060042 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5114-6398","authenticated-orcid":false,"given":"Adrian","family":"Tulbure","sequence":"additional","affiliation":[{"name":"Informatics and Electronics Department, \u201c1 Decembrie 1918\u201d University of Alba Iulia, 510009 Alba Iulia, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6409-9466","authenticated-orcid":false,"given":"Catalin J.","family":"Iov","sequence":"additional","affiliation":[{"name":"Department of Electronics, Telecommunications and Information Technology, \u201cGh. Asachi\u201d Technical University of Ia\u0219i, 700050 Ia\u0219i, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e15","DOI":"10.1192\/bjo.2018.86","article-title":"Digital mental health apps and the therapeutic relationship in psychotherapy: A systematic review","volume":"5","author":"Henson","year":"2019","journal-title":"BJPsych Open"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e13415","DOI":"10.2196\/13415","article-title":"Web-based interventions to improve mental health in home caregivers of people with dementia: Meta-analysis","volume":"21","author":"Zhao","year":"2021","journal-title":"J. Med. Internet Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e14567","DOI":"10.2196\/14567","article-title":"Objective user engagement with mental health apps: Systematic search and panel-based usage analysis","volume":"21","author":"Baumel","year":"2019","journal-title":"J. Med. Internet Res."},{"key":"ref_4","unstructured":"World Health Organization (2019). Mental Health in the Workplace, World Health Organization. Available online: https:\/\/www.who.int\/publications\/i\/item\/9789240053052."},{"key":"ref_5","unstructured":"OECD\/European Union (2018). Health at a Glance: Europe 2018-State of Health in the EU Cycle, OECD Publishing."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1146\/annurev-clinpsy-032816-044949","article-title":"Personal sensing: Understanding mental health using ubiquitous sensors and machine learning","volume":"13","author":"Mohr","year":"2017","journal-title":"Annu. Rev. Clin. Psychol."},{"key":"ref_7","first-page":"269","article-title":"The growing field of digital psychiatry: Current evidence and the future of apps, social media, chatbots, and virtual reality","volume":"20","author":"Torous","year":"2019","journal-title":"World Psychiatry"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"n71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 statement: An updated guideline for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"102331","DOI":"10.1016\/j.nicl.2020.102331","article-title":"Emotion self-regulation training in major depressive disorder using simultaneous real-time fMRI and EEG neurofeedback","volume":"27","author":"Zotev","year":"2020","journal-title":"NeuroImage Clin."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1017\/S0033291721004396","article-title":"Efficacy of bio- and neurofeedback for depression: A meta-analysis","volume":"52","author":"Grassi","year":"2022","journal-title":"Psychol. Med."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Mihai, A.S., Geman, O., Toderean, R., Miron, L., and SharghiLavan, S. (2025). The next frontier in brain monitoring: A comprehensive look at in-ear EEG electrodes and their applications. Sensors, 25.","DOI":"10.3390\/s25113321"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"116761","DOI":"10.1016\/j.neuroimage.2020.116761","article-title":"The shaky ground truth of real-time phase estimation","volume":"214","author":"Zrenner","year":"2020","journal-title":"NeuroImage"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Gordon, P.C., D\u00f6rre, S., Belardinelli, P., Stenroos, M., Zrenner, B., Ziemann, U., and Zrenner, C. (2021). Prefrontal theta-phase synchronized brain stimulation with real-time EEG-triggered TMS. Front. Hum. Neurosci., 15.","DOI":"10.3389\/fnhum.2021.691821"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e7","DOI":"10.1192\/j.eurpsy.2019.7","article-title":"Electroencephalography-based neurofeedback as treatment for post-traumatic stress disorder: A systematic review and meta-analysis","volume":"63","author":"Steingrimsson","year":"2020","journal-title":"Eur. Psychiatry"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1177\/1550059419849170","article-title":"Evaluation of neurofeedback for posttraumatic stress disorder related to refugee experiences using self-report and cognitive ERP measures","volume":"51","author":"Askovic","year":"2020","journal-title":"Clin. EEG Neurosci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"116099","DOI":"10.1016\/j.neuroimage.2019.116099","article-title":"Wearable neuroimaging: Combining and contrasting MEG and EEG","volume":"201","author":"Boto","year":"2019","journal-title":"NeuroImage"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4785","DOI":"10.1038\/s41467-019-12486-x","article-title":"A tool for functional brain imaging with lifespan compliance","volume":"10","author":"Hill","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"14873","DOI":"10.1038\/s41598-021-94328-9","article-title":"The effects of positive or negative self-talk on the alteration of brain functional connectivity by performing cognitive tasks","volume":"11","author":"Kim","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1111\/pcn.13356","article-title":"Heart rate variability in patients with anxiety disorders: A systematic review and meta-analysis","volume":"76","author":"Cheng","year":"2022","journal-title":"Psychiatry Clin. Neurosci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1007\/s12525-021-00458-3","article-title":"Understanding continued smartwatch usage: The role of emotional as well as health and fitness factors","volume":"31","author":"Siepmann","year":"2021","journal-title":"Electron. Mark."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2578","DOI":"10.1017\/S0033291717001003","article-title":"The effect of heart rate variability biofeedback training on stress and anxiety: A meta-analysis","volume":"47","author":"Goessl","year":"2017","journal-title":"Psychol. Med."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1038\/s41746-024-01187-5","article-title":"Understanding activity and physiology at scale: The Apple Heart & Movement Study","volume":"7","author":"Truslow","year":"2024","journal-title":"npj Digit. Med."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.jad.2022.03.048","article-title":"Corrigendum to \u201cTrends of college students\u2019 mental health from 2005 to 2019 and its rural\u2013urban disparities in China\u201d","volume":"306","author":"Sun","year":"2022","journal-title":"J. Affect. Disord."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"110354","DOI":"10.1016\/j.engappai.2025.110354","article-title":"Depression and anxiety detection based on serialized facial expression imitation","volume":"149","author":"Lu","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e14251","DOI":"10.1111\/psyp.14251","article-title":"Relaxing effects of music and odors on physiological recovery after cognitive stress and unexpected absence of multisensory benefit","volume":"60","author":"Baccarani","year":"2023","journal-title":"Psychophysiology"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ko\u0142odziej, M., Majkowski, A., Rak, R.J., Tarnowski, P., Ko\u0142odziej, M., and D\u0142ugosz, M. (2024). Acquisition and analysis of facial electromyographic signals for emotion recognition. Sensors, 24.","DOI":"10.3390\/s24154785"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hnatiuc, M., Geman, O., Avram, A.G., Gupta, D., and Shankar, K. (2021). Human signature identification using IoT technology and gait recognition. Electronics, 10.","DOI":"10.3390\/electronics10070852"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1007\/s40820-023-01235-x","article-title":"Artificial intelligence meets flexible sensors: Emerging smart flexible sensing systems driven by machine learning and artificial synapses","volume":"16","author":"Sun","year":"2023","journal-title":"Nano-Micro Lett."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Sheikh, M., Qassem, M., and Kyriacou, P.A. (2021). Wearable, environmental, and smartphone-based passive sensing for mental health monitoring: A review. Front. Digit. Health, 3.","DOI":"10.3389\/fdgth.2021.662811"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Li, K., Cardoso, C., Moctezuma-Ramirez, A., Elgalad, A., and Perin, E. (2023). Heart rate variability measurement through a smart wearable device: Another breakthrough for personal health monitoring?. Int. J. Environ. Res. Public Health, 20.","DOI":"10.20944\/preprints202308.0732.v1"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Xu, X., Xu, J., Du, R., and Xu, T. (2025). Research on depression recognition model and its temporal characteristics based on multiscale entropy of EEG signals. Entropy, 27.","DOI":"10.3390\/e27020142"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"100162","DOI":"10.1016\/j.neuri.2024.100162","article-title":"Impact of mental arithmetic task on the electrical activity of the human brain","volume":"4","author":"Azizi","year":"2024","journal-title":"Neurosci. Inform."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Kozulin, I., Merkulova, E., Savostyanov, V., Shi, H., Wang, X., Bocharov, A., and Savostyanov, A. (2025). Predicting major depressive disorder using neural networks from spectral measures of EEG data. Bioengineering, 12.","DOI":"10.3390\/bioengineering12111251"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"e36991","DOI":"10.1016\/j.heliyon.2024.e36991","article-title":"MDD brain network analysis based on EEG functional connectivity and graph theory","volume":"10","author":"Chen","year":"2024","journal-title":"Heliyon"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"120751","DOI":"10.1016\/j.jad.2025.120751","article-title":"The role of frontal EEG in predicting clinical response of major depressive disorder to intranasal ketamine and esketamine","volume":"395","author":"Trenado","year":"2026","journal-title":"J. Affect. Disord."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"18003","DOI":"10.1209\/0295-5075\/107\/18003","article-title":"Mood states modulate complexity in heartbeat and EEG dynamics: A multiscale entropy analysis","volume":"107","author":"Valenza","year":"2014","journal-title":"Europhys. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Valenza, G., Garcia, R.G., Citi, L., Scilingo, E.P., Tomaz, C., and Barbieri, R. (2015). Nonlinear digital signal processing in mental health: Characterisation of major depression using instantaneous entropy measures of heartbeat and EEG dynamics. Front. Physiol., 6.","DOI":"10.3389\/fphys.2015.00074"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Chen, W., Chen, H., Jiang, W., Chen, C., Xu, M., Ruan, H., Chen, H., Yu, Z., and Chen, S. (2025). Heart rate variability and heart rate asymmetry in adolescents with major depressive disorder during nocturnal sleep period. BMC Psychiatry, 25.","DOI":"10.1186\/s12888-025-06911-3"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"103381","DOI":"10.1016\/j.compbiomed.2019.103381","article-title":"Detection of major depressive disorder from linear and nonlinear heart rate variability features during mental task protocol","volume":"112","author":"Byun","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1433","DOI":"10.1017\/S0033291719001302","article-title":"Attenuated vagally-mediated heart rate variability at rest and in response to postural maneuvers in patients with generalized anxiety disorder","volume":"50","author":"Chang","year":"2020","journal-title":"Psychol. Med."},{"key":"ref_41","first-page":"45","article-title":"Can Circadian Rhythms of Heart Rate Variability Identify Major Depressive Disorder? A Machine Learning Approach","volume":"355","author":"Kim","year":"2024","journal-title":"J. Affect. Disord."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1109\/TBME.2021.3088218","article-title":"Robust R-peak detection in low-quality Holter ECGs using 1D convolutional neural network","volume":"69","author":"Zahid","year":"2022","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"e40342","DOI":"10.2196\/40342","article-title":"When heart beats differently in depression: Review of nonlinear heart rate variability measures","volume":"10","author":"Sidorova","year":"2023","journal-title":"JMIR Ment. Health"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1038\/s44184-025-00130-0","article-title":"Electrocardiography-derived autonomic profiles in depression and suicide risk with insights from the UK Biobank","volume":"4","author":"Weber","year":"2025","journal-title":"npj Ment. Health Res."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Behradfar, M., Roy, S., and Nuamah, J. (2025). Optimizing mental stress detection via heart rate variability feature selection. Sensors, 25.","DOI":"10.3390\/s25134154"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.jpsychires.2017.09.012","article-title":"Depression recognition according to heart rate variability using Bayesian networks","volume":"95","author":"Kuang","year":"2017","journal-title":"J. Psychiatr. Res."},{"key":"ref_47","first-page":"106632","article-title":"Identification of major depression patients using machine learning models based on heart rate variability during sleep stages for pre-hospital screening","volume":"154","author":"Geng","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Yang, Y., Chen, F., and Liu, J. (2024). Heart rate variability-based machine learning for auxiliary diagnosis of depression. BMC Psychiatry, 24.","DOI":"10.1186\/s12888-024-06384-w"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.jad.2023.05.022","article-title":"Heart rate variability in children and adolescents with major depressive disorder: A systematic review and meta-analysis","volume":"335","author":"Chen","year":"2023","journal-title":"J. Affect. Disord."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1007\/s40846-022-00687-7","article-title":"End-to-end depression recognition based on a one-dimensional convolution neural network model using two lead ECG signal","volume":"42","author":"Zang","year":"2022","journal-title":"J. Med. Biol. Eng."},{"key":"ref_51","first-page":"123","article-title":"Circadian rhythm-based HRV features for the classification of major depressive disorder","volume":"172","author":"Xia","year":"2025","journal-title":"J. Psychiatr. Res."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Alzate, M., Torres, R., De la Roca, J., Quintero-Zea, A., and Hernandez, M. (2024). Machine learning framework for classifying and predicting depressive behavior based on PPG and ECG feature extraction. Appl. Sci., 14.","DOI":"10.3390\/app14188312"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1126\/science.aax2342","article-title":"Dissecting racial bias in an algorithm used to manage the health of populations","volume":"366","author":"Obermeyer","year":"2019","journal-title":"Science"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1544","DOI":"10.1001\/jamainternmed.2018.3763","article-title":"Potential biases in machine learning algorithms using electronic health record data","volume":"178","author":"Gianfrancesco","year":"2018","journal-title":"JAMA Intern. Med."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1002\/wps.20882","article-title":"The promise of machine learning in predicting treatment outcomes in psychiatry","volume":"20","author":"Chekroud","year":"2021","journal-title":"World Psychiatry"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"874","DOI":"10.1056\/NEJMms2004740","article-title":"Hidden in plain sight-Reconsidering the use of race correction in clinical algorithms","volume":"383","author":"Vyas","year":"2020","journal-title":"N. Engl. J. Med."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Sogancioglu, G., Mosteiro, P., Salah, A.A., Scheepers, F., and Kaya, H. (2024, January 21\u201323). Fairness in AI-based mental health: Clinician perspectives and bias mitigation. Proceedings of the AAAI\/ACM Conference on AI, Ethics, and Society (AIES 2024), San Jose, CA, USA.","DOI":"10.1609\/aies.v7i1.31732"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1146\/annurev-biodatasci-092820-114757","article-title":"Ethical machine learning in healthcare","volume":"4","author":"Chen","year":"2021","journal-title":"Annu. Rev. Biomed. Data Sci."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"866","DOI":"10.7326\/M18-1990","article-title":"Ensuring fairness in machine learning to advance health equity","volume":"169","author":"Rajkomar","year":"2018","journal-title":"Ann. Intern. Med."},{"key":"ref_60","first-page":"1","article-title":"Gender shades: Intersectional accuracy disparities in commercial gender classification","volume":"81","author":"Buolamwini","year":"2018","journal-title":"Proc. Mach. Learn. Res."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1056\/NEJMp1714229","article-title":"Implementing machine learning in health care-Addressing ethical challenges","volume":"378","author":"Char","year":"2018","journal-title":"N. Engl. J. Med."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1980","DOI":"10.1038\/s41598-021-81368-4","article-title":"Predictive modeling of depression and anxiety using electronic health records and a novel machine learning approach with artificial intelligence","volume":"11","author":"Nemesure","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Boyer, F., Oueghlani, D., and Boudaoud, S. (2023). Reducing noise, artifacts and interference in single-channel EMG signals: A review. Sensors, 23.","DOI":"10.20944\/preprints202302.0052.v1"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"7768","DOI":"10.1038\/s41598-023-33319-4","article-title":"A smart approach to EMG envelope extraction and powerful denoising for human\u2013machine interfaces","volume":"13","author":"Esposito","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Schlink, B.R., Haeufle, D.F., Schmitz, J., and Blickhan, R. (2020). Comparison of signal processing methods for removing motion artifacts from high-density EMG signals. Sensors, 20.","DOI":"10.1109\/OJEMB.2020.2999782"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Lu, Y., Zhang, H., and Sun, J. (2024). Improved multi-layer wavelet transform and blind source separation for ECG artifact suppression in surface EMG signals. Front. Bioeng. Biotechnol., 12.","DOI":"10.3389\/fbioe.2024.1367929"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1186\/s12984-023-01268-8","article-title":"Review of electromyography onset detection methods for real-time control of robotic exoskeletons","volume":"20","author":"Carvalho","year":"2023","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Luijcks, R., Hermens, H.J., Bodar, L., Vossen, C.J., van Os, J., and Lousberg, R. (2014). Experimentally induced stress validated by EMG activity. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0095215"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"827","DOI":"10.4028\/www.scientific.net\/AMR.709.827","article-title":"Stress emotion recognition based on RSP and EMG signals","volume":"709","author":"Wei","year":"2013","journal-title":"Adv. Mater. Res."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.3390\/biomedinformatics4020058","article-title":"A comprehensive analysis of trapezius muscle EMG activity in relation to stress and meditation","volume":"4","author":"Ahmed","year":"2024","journal-title":"BioMedInformatics"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"105482","DOI":"10.1016\/j.cmpb.2020.105482","article-title":"Stress detection using ECG and EMG signals: A comprehensive study","volume":"193","author":"Pourmohammadi","year":"2020","journal-title":"Comput. Methods Programs Biomed."}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/2\/165\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T09:25:18Z","timestamp":1769678718000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/2\/165"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,29]]},"references-count":71,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["bioengineering13020165"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering13020165","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,29]]}}}